Environment×Combining Ability Interaction for Quality Traits in Tomato (Solanum lycopersicum L.)

The multilocational studies were conducted to evaluate 21 hybrids of tomato in Pratapgarh(Uttar Pradesh), Varanasi (Uttar Pradesh) and Burdwan (West Bengal), India during August–February of 2015–16 and 2016–17. Data on four quality traits viz., total soluble solids, titrable acidity, carotene content and lycopene content were estimated. Combined analysis of variance (ANOVA) revealed significant effects of locations, genotypes, genotype-location interaction and parents vs hybrids-location interaction for all the traits. The significance of combining ability effects (general combining ability and specific combining ability) and their interaction with location revealed environmental influences on combining ability effects for all the traits. The Present study indicated higher estimates of to , lower estimates of heritability and GCA/SCA ratio for quality traitsandthereby implied preponderance of non-additive gene action in determination of quality traits studied. Desirable parental lines were identified for different traits like EC 620438 and BS 24-2 for TSS, BS 24-2 and Superbug for titrable acidity, Columbia, EC 620438 and Superbug for carotene content, and H 86 and EC 620541 for lycopene content. Similarly, desirable specific combiners for different traits were identified. The hybrid combination EC 620438×BS 24-2 appeared good specific combiner for TSS, carotene and lycopene content. The parental lines/hybrids identified in present study will be useful in identifying hybrids adapted to a range of environments.

Discovery of Genomic Regions Associated With Resistance to Late Wilt Disease Caused by Harpophora Maydis (Samra, Sabet and Hing) in Maize (Zea mays L.)

Late wilt disease (LWD) caused by Harpophora maydis (Samra, Sabet and Hing) is emerging as major production constraint in maize across the world. As a prelude to develop maize hybrids resistance to LWD, genetic basis of resistance was investigated. Two F2:3 mapping populations (derived from CV156670 × 414-33 (P-1) and CV156670 × CV143587 (P-2)) were challenged with LWD at two locations (Kallinayakanahalli and Muppadighatta) during 2017 post-rainy season. Wider range of LWD scores were observed at both locations in both the populations. LWD response was influenced by significant Genotype × location interaction. Six and 56 F2:3 progeny families showed resistance level better than resistant parent. 150 and 199 polymorphic SNP markers were used to genotype P-1 and P-2, respectively. Inclusive composite interval mapping was performed to detect significant QTL, QTL × QTL, QTL × Location interaction effects. Three major and four minor QTL controlling LWD resistance were detected on chromosome-1. Position and effect of the QTL varied with the location. Significant di-QTL interactions involving QTL (with significant and/or non-significant effects) located within and between all the ten chromosomes were detected. Five of the seven detected QTL in our study showed significant QTL × location interaction. Though two major QTL (q-lw-1.5 and q-lw-1.6) with lower Q×L interaction effects could be considered as stable, their phenotypic variance is not large enough to deploy them in MAS. Based on these results, strategies to breed maize for resistance to LWD are discussed.

Genetic worth of multiple sets of cowpea breeding lines destined for advanced yield testing

The objective of this study was to determine genetic potentials in eight sets of cowpea lines for grain yield (GY), hundred seed weight (HSDWT) and days to 50% flowering (DT50FL). A total of 614 F6 genotypes constituting the sets, grouped by maturity, were evaluated across two locations in Northern Nigeria, in an alpha lattice design, two replications each. Data were recorded on GY, HSDWT and DT50FL.Variance components, genotypic coefficient of variation (GCV), and genetic advance (GA) were used to decode the magnitude of genetic variance within and among sets. Genetic usefulness (Up) which depends on mean and variance to score the genetic merits in historically bi-parental populations was applied to groups of breeding lines with mixed parentage. Principal component analysis (PCA) was used to depict contribution of traits to observed variations. GY and DT50FL explained the variance within and between sets respectively. Genotypes were significantly different, although genotype-by-location and set-by-location interaction effects were also prominent. Genetic variance (δ2G) and GCV were high for GY in Prelim2 (δ2G = 45,897; GCV = 19.58%), HSDWT in Prelim11 (δ2G = 7.137; GCV = 17.07%) and DT50F in Prelim5 (δ2G = 4.54; GCV = 4.4%). Heritability varied among sets for GY (H = 0.21 to 0.57), HSDWT (H = 0.76 to 0.93) and DT50FL (H = 0.20 to 0.81). GA and percentage GA (GAPM) were high for GY in Prelim2 (GAPM = 24.59%; GA = 269.05Kg/ha), HSDWT in Prelim11 (GAPM = 28.54%; GA = 4.47 g), and DT50F in Prelim10 (GAPM = 6.49%; GA = 3.01 days). These sets also registered high values of genetic usefulness, suggesting potential application in non-full sib populations. These approaches can be used during preliminary performance tests to reinforce decisions in extracting promising lines and choose among defined groups of lines.

The Quality of Interactions Between Staff and Residents With Cognitive Impairment in Nursing Homes

Background: Positive and effective staff–resident interactions are imperative to adequately assess and meet the needs of cognitively impaired residents in nursing homes and optimize their quality of life. Purpose: The purpose of this study was to quantify, describe, and analyze the interaction between staff and cognitively impaired residents in nursing homes, using the Quality of Interaction Schedule (QuIS). Specifically, the following aims were addressed— Aim 1: To quantify and describe the quality of interactions between staff and cognitively impaired residents in nursing homes. Aim 2: To analyze whether the quality of staff–resident interactions vary by resident cognitive status (moderate vs severe) and interaction characteristics (interaction location, interaction situation, interpersonal distance, type of staff, and resident level of participation). Method: This descriptive analysis utilized baseline data from the first 2 cohorts in a randomized clinical trial including 341 residents from 35 nursing homes. Results: Five hundred fifty-six staff–resident interactions were evaluated; majority were positive (n = 466, 83.8%) and the remaining were either neutral (n = 60, 10.8%) or negative (n = 30, 5.4%). The quality of interactions varied by interaction location, interpersonal distance, and resident participation. Conclusion: This study provides some current descriptive information about the quality of staff-resident interactions in nursing homes and the interaction characteristics that might impact these interactions. Future research should focus on decreasing the negative/neutral interactions and explore staff characteristics (e.g., gender, level of experience) and facility factors (e.g., size, ownership) that might influence the quality of interactions.

PSVII-18 Fatty acid composition of the longissimus muscle from Angus and Wagyu sired cattle at the 6th and 12th rib location

The fatty acid (FA) composition of the longissimus muscle (LM) of Angus and Wagyu sired cattle raised to a similar body weight (612 kg) were compared at the 6th and 12th rib locations. Angus sired steers represented T1, cattle from a Wagyu sire selected for growth represented T2, and cattle from a Wagyu sire selected for marbling represented T3. Data were analyzed mixed model with repeated measurements on animal (LM location); the model include the fixed effect of treatment, LM location, and their interaction, and random effect of sex. The percentage of 16:0, 18:1cis9, 18:3, and monounsaturated FA (MUFA) exhibited a treatment*LM location interaction (P ≤ 0.7), where T2 cattle had a greater percentage of 16:0 and a lesser percentage of 18:1cis9, 18:3, and MUFA at the 12th rib vs. 6th rib location compared with T1 and T3 cattle. The percentage of total FA lipid, polyunsaturated FA(PUFA), and PUFA:SFA ratio in the LM were greater (P ≤ 0.02) for T3 cattle compared with T1 and T2 cattle. The percentage of 18:0 was greater (P ≤ 0.01) for T1 cattle compared with T2 and T3 cattle, while T1 cattle had a greater (P ≤ 0.01) percentage of saturated FA (SFA) compared to T3 cattle. The percentage of 18:1cis9, other 18:1cis isomers, 18:2, MUFA, and MUFA:SFA ratio were greater (P ≤ 0.02) for T3 cattle compared with T1 cattle, with T2 cattle being intermediate. The percentage of total FA lipid, 18:0, 18:1 trans isomers, and SFA were greater (P ≤ 0.01) at the 6th rib LM location, while 14:1, 18:cis9, other 18:1 cis isomers, MUFA, MUFA:SFA, and PUFA:SFA ratio were greater (P ≤ 0.02) at the 12th rib LM location

Genetic Variability and Genotype X Environment Interactions Effect on Grain Iron (Fe) and Zinc (Zn) Concentration in Lentils and Their Characterization under Terai Environments of Nepal

Billions of peoples are directly affected from the micronutrient malnutrition called hidden hunger affecting one in three people. Micronutrient Iron (Fe), and zinc (Zn) deficiencies affect large numbers of people worldwide. Iron (Fe) deficiency leads to maternal mortality, mental damage and lower disease resistant of children. Likely Zinc (Zn) deficiency is responsible for stunting, lower respiratory tract infections, and malaria and diarrhea disease in human beings. Nepalese lentils are in fact rich sources of proteins and micronutrients (Fe, Zn) for human health and straws as a valuable animal feed. It has ability to sequester N and C improves soil nutrient status, which in turn provides sustainable production systems. Twenty five lentil genotypes were evaluated to analyze genotype × environment interaction for iron and zinc concentration in the grains. Analysis of variance (ANOVA) indicated that the accessions under study were found varied significantly (P=<0.001) for both seed Fe and Zn concentrations at all the three locations. Pooled analysis of variance over locations displayed highly significant (at P=<0.001) differences between genotypes, locations and genotype × location interaction for Zn micronutrient but insignificant genotype x location interaction was found in Fe micronutrient. Among 25 genotypes, the ranges for seed Fe concentration were 71.81ppm (ILL-2712)-154.03 ppm (PL-4) (mean 103.34 ppm) at Khajura, 79.89 ppm (ILL-3490)-128.14 ppm (PL-4) (mean 95.43 ppm) at Parwanipur, and 83.92 ppm (ILL-7979) -137.63 ppm (ILL-6819) (mean 103.11ppm) at Rampur, while the range across all the three locations was 82.53 ppm (ILL-7979) -133.49 ppm (PL-4) (mean 101.04 ppm). Likely the range for seed Zn concentration was 53.76 ppm (ILL-7723) – 70.15 ppm (ILL-4605) (mean 61.84 ppm) at Khajura, while the ranges for Parwanipur and Rampur were 54.21 ppm (ILL-7723) -91,94 ppm (ILL-4605) (mean 76.55 ppm) and 46.41 ppm (LG-12) – 59.95 ppm (ILL-4605) (mean 54.27 ppm) , respectively. The range across the three environments was 54.03 ppm (ILL-7723) – 75.34 ppm (HUL-57) (mean 64.22 ppm). Although both the micronutrients were influenced by environment, seed Fe was more sensitive to environmental fluctuations in comparison to seed Zn concentration. The G × E study revealed that it was proved that genotypes Sagun, RL-6 and LG-12 were more stable for seed Fe concentration and genotypes WBL-77, ILL-7164, RL-11 were found more stable for seed Zn concentration. In the AMMI analysis employing Gollob’s test, first two PC explained 100% of the G × E variation. PC 1 and PC 2 explained 87.19% and 12.81% of total G × E interactions for Fe concentration and likely for Zn concentration; PC1 and PC2 explained 70.11% and 29.88%, respectively. The critical perusal of biplot revealed that Parawnipur locations was found to discriminating power for Fe concentration while for Zn concentration Khajura location was found to be most discriminative. The critical analysis of pedigree vis-à-vis micronutrient concentration did not reveal any correlation. This is probably the first report on iron and zinc concentration in lentil from Nepal.

Soil Macronutrient Responses in Diverse Landscapes of Southern Tallgrass to Two Stocking Methods

Macronutrient (N, P, S, K, Ca, and Mg) availability and distribution in soils of grassland ecosystems are affected by diverse factors, including landscape position, climate, and forms of management. This study examined flux in plant-available macronutrients in production-scale (60 to 80 ha) paddocks of southern tallgrass prairie of central Oklahoma, United States, managed (2009–15) under two contrasting stocking methods (continuous yearlong; rotational stocking among 10 sub-paddocks). Macronutrient availability within the 0–7.5 cm and 7.5–15 cm soil depths were determined with sets of anion-cation exchange membrane probes at 16 locations within paddocks, oriented along transects from water sources to far corners. No clear overall effect related to stocking method was recorded for all macronutrient distributions. The only significant stocking method × location interaction occurred for K (p = 0.01). All other macronutrients displayed significant (p < 0.08) location effects that were common across stocking methods. Effects relatable to stocking method occurred in interactions with soil depth or time of year (p < 0.10), but responses of macronutrient flux to stocking method in these interactions varied. Higher flux occurred in available S, Ca, and Mg in proximity (<24 m) to water sources, which may be related to grazing, but local features of the landscape may also have been involved. More attention to landscape features included within paddocks, and standardized organization of water and other features within paddocks, would improve the potential to define grazing effects on macronutrient distribution.

Exploring Mobile Game Interactions

The mobile game industry has been growing rapidly in both the number of games and revenues. Choosing the right interactions for a game has become a major challenge for developers. Some developers use inappropriate interactions in their games which causes them to be less fun than they should be. This research focuses on gathering and defining possible mobile game interactions so as to guide and enable designers and developers to choose the right interactions for their games. The researchers have extensively reviewed and explored various mobile game interactions both through research studies and through existing mobile games. Subsequent to observations, mobile game interactions were then categorized as follows: 1) Touch interaction 2) Motion/Movement interaction 3) Video interaction 4) Sound interaction 5) Special purpose interaction 6) Location interaction 7) Electroencep-halography (EEG) interaction 8) Date/Time interaction 9) Weather interaction 10) Light interaction 11) Proximity interaction 12) Network interaction 13) Social interaction and 14) Bioinformatics interaction. These 14 interactions can be used to support gameplay, ideas, and innovation of mobile games.